Trust and relationship between corruption and economic growth

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Trust and the Relationship between Corruption and Economic

Growth

University of Amsterdam

Bachelor’s thesis: Economics

Min Zhang

Student Number: 10389814

Supervisor: Audrey Hu

June 2015

Abstract

The relationship between corruption and economic growth has long been a topic of common interest. The majority of cross-‐country studies show that corruption has negative effect on economic growth. However, this result is not universally robust (Vaal & Ebber, 2011). The present study looks deeper into this issue and investigates how a nation’s public trust level intertwines with the corruption-‐growth relationship. In particular, the study conducts a cross-‐country test based on Li and Wu (2010)’s hypothesis that corruption hinders the economic growth less when the nation’s incipient public trust level is higher. The results, contrary to Li and Wu’s finding, suggest that public trust has negligible impact on corruption-‐economic growth relationship. Several possible explanations of this difference will be listed, including the essence of such an analysis.

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Statement of Originality

The document is written by Min ZHANG who declares to take full responsibility

for the contents of this document.

I declare that the text and the work presented in this document is original and

that no sources other than those mentioned in the text and its references have

been used in creating it.

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1. Introduction

Corruption is a widespread phenomenon, with many countries experiencing increasing corrupted behaviors. According to Transparency International1_{(2012),
corruption
has}

been rampant in over 70 countries including several rapidly growing economies such as China.

Conceivably, the effect of corruption on economic growth has been a topic of interest to the general public and specialists. One stream advocates the standard view that corruption will hamper economic growth because it reduces the efficient allocation of resources and simultaneously creates distortions in the economy.2_{In
particular,}

Tanzi and Davoodi (1997) claim that corruption harms economy in four ways: lower quality of public facilities, lower expenditures on business operation and infrastructure, lower government revenue and higher public investment. Additionally, Shleifer and Vishny (1993) emphasize that corrupted governments tend to primarily consider and give approval to projects that offer a bribery opportunity instead of selecting those that have high potential to produce larger economic welfare. This may cause a problem of inefficient resource allocation: in order to proffer the bribery, firms may divert the resources that would otherwise been invested in product improvement. However, the other school of thought points out that corruption does not necessarily lead to lower economic performance and it may be conducive to economic growth instead. They question the assumptions3_{made
in
standard
view
and
argue
that
rent-‐seeking
behavior}

would on the other hand incentivize government officials to make efforts and propose worthwhile projects, thus increase social welfare (Cowen et al., 1994).4_{Similarly,
Beck}

and Maher (1986) presents a view that corruption may occasionally promote growth because bribery can be viewed as working like piece-‐rate bonus for government officials, thus induces more efficient government services.

Whatever stance one holds, corruption remains of great concerns thus presents itself as a fascinating and ongoing research topic. Recently, Vaal and Ebben (2011) point out that a major shortcoming for most corruption literatures is that they disregard the

1_{It
is
an
international
not-‐for-‐profit
organization
that
leads
a
primary
role
in
fighting
against
corruption.
It}

estimates a nation’s corruption level each year based on surveys. WWW.TRANSPARENCY.ORG

2_{For
example,
see
Svensson
(2003),
Kaufmann
and
Wei
(2000),
Rose-‐Ackerman
(1997)
provide
a}

theoretical evidence of corruption’s negative role in economic growth.

3_{For
instance,
they
assume
that
government
aims
to
promote
public
economic
welfare
(Leff,
1964).} 4_{Cowen
et
al.
(1994)
argue
that
rent-‐seeking
behavior
in
government
can
be
beneficial
by
using}

principal-‐agent model. They assume that bargain takes place after instead of before the agent make an action. In this model, mayor is the principal and the person who devises and implements projects is the agent.

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importance of institutional environment5_{.
With
mathematical
proof,
they
find
that
in
an}

institutional vacuum where institutional environment is not taken into consideration, growth will be depressed by the reduced labor input and productive public goods. Whereas when the institution environment is considered, the impact of corruption on growth is ambiguous6_{.
This
implies
that
institution
characterized
by
economic,
political}

and social factors plays an important role in examining the effect of corruption on economic growth (Myerson, 2006). Presently, there are several empirical studies that take economic and political factors into account7_{.
For
example,
Meon
and
Sekkat
(2005),}

Aidt, Dutta and Sena (2006) have interacted political and economic institutional factors with corruption-‐economic growth relation and obtained mixed results. However, most of the studies do not consider social institutional factors. Acknowledged by Coleman (1990), social institution is characterized by a set of inherent social-‐structural assets, for example trust, norms, and networks. Based on this, I highlight public trust as an important feature of social institution and incorporate it into the corruption-‐growth analysis.

The purpose of this paper is to identify a complementary relationship between corruption and the role of public trust and its influence on economic growth. In order to achieve that goal, a cross-‐section of 41 nations is examined. Firstly, a model of the impact of corruption on economic growth is estimated, without controlling the public trust level. However, the results show that the conventional negative impact of corruption disappears for this model specification. Therefore, a structural break model8

is then estimated, controlling for a complementary relationship between corruption and public trust as measured by World Value Survey index9_{.
The
main
finding
is
that}

corruption has an insignificant positive effect on economic growth. The results imply that highly corrupted countries such as China that enjoys constantly remarkable economic growth over the past two decades cannot contribute it to its high level of high trust. Instead it may due to other reasons such as the essence of corruption. Several possible explanations will be listed.

The rest of this paper is structured as follows. Section 2 briefly reviews the related

5_{North
(1990)
defines
institutions
as
formal
or
informal
regulations
inherited
in
a
society.
They
function
in}

such a way to achieve a stable environment for social activities including human interactions.

6_{See
part
2.1
for
further
details.}

7_{E.g
Mo
(2001),
Mauro
(1995),
Shera
(2014)}

8_{See
Markwardt
(2009)
estimates
a
structural
break
model
to
examine
the
role
of
effectiveness
of}

monitoring bureaucrats’ behavior in the decentralization and corruption relationship.

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theoretical and empirical literature and public trust. Section 3 presents the data and the reason why they are included. Section 4, which forms the core of this paper, presents the methodology and empirical analysis. Lastly, section 5 concludes with final remarks and limitations of this study.

2. Literature Review

2.1 Background and theoretical considerations

Theoretically, the model concerning the effects of corruption on economic growth does not reach any agreement (Vaal & Ebber, 2011). Using neoclassical microeconomic theory10_{,
the
effect
of
corruption
on
economic
performance
can
be
illustrated
by
a}

simple supply and demand framework based on the competition efficiency concept. As presented in Figure 1: situation (a) shows that with the perfect competition assumption in neoclassical economic theory, price is set at a point where marginal cost equals to average revenue (MC=AR). In that case, market efficiency is achieved and total surplus is maximized. However, if the assumption is violated, then economy inefficiency arises with a deadweight loss shown as the shaded area in situation (b). Corruption is one example of distortions for competition (Johnston, 1997). When suppliers pay a bribe, the procedure for doing business will be faster and cheaper than normally. Assume an extreme case in which a supplier can establish a monopolistic situation through paying a bribe. As a single supplier, his profit can be maximized by setting the price where marginal cost equals marginal revenue (MC=MR), resulting in an economic rent obtained by the supplier and a deadweight loss in the industry. Scaling this microeconomic phenomenon to the macroeconomic view, it can be argued that inefficiency resulted from corruption might undermine economic growth.

Taking institutional framework into account, Vaal and Ebben (2011) construct a theoretical model and consider three institutional features namely political system, political stability and property rights in their analysis of corruption and growth. By building a two-‐layer model: one studies the direct effect of corruption on growth by assuming in an institutional vacuum (i.e. implying no institutional factors included in the

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model) and the other one by incorporating institutions to recognize the direct and indirect impacts of corruption on growth, their find that a country’s institutional context is crucial in determining the overall effect of corruption on economic growth. In particular, the effect of corruption depends on the nation’s incipient level of property rights or political stability. For example, when the existing property rights protection is effective or the political environment is stable, corruption can adversely affect economic growth because of the distortion such as misallocation of resources. However, in case of a highly unstable political environment or lacking property right protection, then corruption may stimulate economy because the negative effects of distortion will be neutralized by the higher marginal benefit of a systems’ property right. Thus, for a less-‐developed institution associated with a low level of property right protection or a high level of political instability, corruption might stimulate economy based on Vaal and Ebben (2011)’s theoretical arguments. This finding supports corruption’s role of “grease the wheels” hypothesis11_{initiated
by
Huntington
(1968)
by
providing
a
deeper}

theoretical foundation.

Moreover, the emergence of Asian Miracle12_{also
questions
the
standard
view
that}

corruption is detrimental to economy (Ali, 2008). For example, a paper published by Transparency International (2012) shows that the level of corruption in China is seemingly high and ranks approximately 80th_{among
all
the
countries.
In
spite
of
the}

rampant corruption, China’s economy has grown rapidly with an average annul growth rate at about ten percent. In addition, other South East Asian economies such as South Korea achieved exponential growth during a period where corruption was widespread as well. Due to this particular phenomenon, academics have sought various ways to dispute the standard view. For example, Leff (1964) claims that the standard view assumes that government officials working together with the goal of promoting economic development. However, in reality, such an assumption is rarely existed. For instance, government officials would have other goals such as self-‐enrichment in mind. While in the absence of rent seeking or corruption, there may be insufficient incentives for selfish bureaucrats to propose efficiency-‐enhancing projects (Cowen et al., 1994). Thus, a revaluation of corruption’ impact is warranted. For example, bribery can provide

11_{“Grease
the
wheels”
in
the
context
of
corruption
implies
that
for
countries
with
a
lower
level
of}

governance quality, corruption is conducive to economy.

12_{Despite
the
higher
level
of
corruption,
some
Asian
countries
such
as
China
still
enjoy
higher
economic}

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business leaders with the opportunity of decision-‐making, and this may promote innovation and result in an increase of economic welfare. Furthermore, Leff (1964) theorizes that corruption can circumvent inefficient rules and delays, serves as “fast money”, thus to facilitate beneficial trades and achieve growth. Put differently, through bribe bidding competition, only those more efficient firms can afford higher bribes, this induces projects to be assigned to the most efficient firms, encouraging higher economic welfare.

Both Vaal & Ebber (2011) and Leff (1964) demonstrate that the institutional environment in which an economy operates is of great importance in the analysis of the corruption-‐economic growth relationship. Based on Li and Wu (2010) and Coleman (1990), this paper hypothesizes that public trust is an important feature of social institution and plays a crucial role in the corruption and economic growth relationship13_.

In particular, for countries with a high level of public trust, corruption hinders economic growth less.

2.2 Empirical studies on corruption and economic growth

The majority of empirical studies have found a negative correlation between corruption and economic growth (Ali, 2003; Del and Papagni, 2001)14_{.
Even
though
some
of
the}

studies consider institutional framework, only economic and political institutional factors such as existing economic statue, democracy and political stability have been considered15_{.
Social
institutional
factors
such
as
public
trust
are
disregarded.
There
are}

numerous studies about corruption, however, for the purpose of this study, only the most relevant empirical studies especially cross-‐country analysis will be reviewed. Mo (2001) estimates the overall effect of corruption on the GDP growth rate and identifies the transmission channels by using cross section analysis. The overall effect is decomposed into three transmission channels: political instability, human capital and investment. By estimating each channel’s effect separately in the context of 45 countries, he finds that a one-‐unit increase of corruption measured by CPI16_{will
lead
to
a
0.545}

percent points reduction in the annual growth rate of GDP. Amongst the three channels,

13_{Coleman
(1990)
points
out
social
institutional
factors
consist
of
trust,
norms
and
network.}

14_{Ali
dhe
Isse
(2003)
adopts
regression
methodology
finds
high
corruption
decreases
economic
growth.}

Del Monte and Papagni (2001) adopt regression and ADL model finds negative impact of corruption. Mauro (1995) uses regression methodology finds negative relationship between corruption and investment.

15_{For
example,
in
Mo
(2001)’s
paper,
independent
variables
only
consider
economic
and
political
factors.} 16_{Corruption
Index
Perception,
a
common
measure
of
corruption
level.
See
Mo
(2001),
Lessmann
(2009).}

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political instability is the most important channel in corruption’s effect on economic growth. It accounts for 52 percent of the overall reduction in the growth rate, while human capital and investment account for 15 percent and 21 percent reduction respectively. In his paper, instrumental variables are also included in the model, however the validity has not been tested properly.

Similarly, Mauro (1995) also identifies a negative relationship between corruption and investments and growth. In particular, he finds that a one-‐unit reduction of corruption index will result in 0.8 percent increase of the annual growth rate of GDP per capita. To remove endogenity, instrumental variable namely index of ethno-‐linguistic fractionalization was introduced as well. The ethno-‐linguistic fractionalization variable is used for measuring the probability that the randomly selected two people in the same country do not come from the same ethno-‐linguistic type.

To test the “greasing the wheel” hypothesis (i.e. corruption is conductive to economic growth), Houston (2007) examines a broad country-‐level data. He finds that corruption could have both expansionary and restrictive economic impacts. The ultimate impact is dependent on the degree of laws, which relate to property-‐protection level in a country. Corruption can instill an expansionary role for a country when the protection is weak: expansionary impact is more than 20 percent of the restrictive impact for countries with weak governance.

2.3 Public trust

The decision to become corrupted or not is based on three factors (Becker, 1968). First, what is the benefit (measured by the size of rewards or payoff) that can be obtained through corruption? Second, what is the cost (measured by the size of the punishments) if corrupted behaviors are detected? Third, how large is the probability for corrupted behaviors to be detected? The level of trust plays an important part in the third factor. La Porta et al. (1997) point out with statistical analysis that trust can enhance cooperation, which will in turn enhance economic performance. In particular, they find that a one-‐standard deviation rise in the level of trust will lead to a 0.7 percent and 0.3 percent increase in judicial efficiency and annual GNP per capita growth respectively. Moreover, the case of China also illustrates this complementary relationship between corruption and trust. China is characterized by its “guanxi” culture (Park and Luo, 2001).“Guanxi” typically indicates the informal social network formed based on personal

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relations. In China, it is relatively easy for people to connect with others who they are not familiar with and maintain a long-‐term relationship with them, and this practice is viewed as a soft ability in daily life. For example, if a child cannot be admitted into a prestigious high school through the regular admission process, the child still has the chance to gain admittance through the irregular process, i.e“guanxi”. To elaborate, the parents can invite the president for an informal dinner or gift the school some form of monetary benefit to foster a good relationship between the parents and the school. If not, the parents could also turn to their friends or colleagues etc. who know the president or the president’s family, friend etc. to approach the president. Through this indirect connection, there is a possibility to get to know the president, form a relationship with him and achieve the goal of gaining admittance into the high school. However, in other countries where the individualistic culture is more prevalent, such “guanxi” cases rarely happen. In regards to public trust, Putnam (1993) points out that public trust is formed throughout the long history of “horizontal networks of association” in a society and it is commonly observed social norms. Naturally, the close informal social network in China has founded a basis for public trust. In an empirical investigation, Serritzlew, Sonderskov and Svendsen (2012) find that trust affects growth in a positive manner: countries, which have a high level of trust, are usually associated with a stable cultural phenomenon, which will positively affect growth through individual behavior and institutional development. Zak & Knack (2001) emphasize that trust has a crucial effect on economic growth because it affects the transaction costs associated with the investment. For countries with a sufficiently low level of trust, economic growth cannot be achieved because the amount of investment being undertaken will decrease significantly, which impedes growth.

Even though the relationship between corruption and economic growth does not reach an agreement in the theoretical literature, numerous empirical studies have found that corruption is inimical in affecting economic growth. The reason for the mixed results in the theoretical literature is that some papers analyze the impact of corruption on economic growth in the context of a country’s specific institutional framework. They argue that corruption’s ultimate impact on economic growth depends on institutional environment. For example, as explained in section 2, countries with a high level of political instability, corruption may be conductive to economic growth. However, due to the complexity of the institutional framework, most of the empirical work neither give a

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comprehensive analysis about its effect in the relation between corruption and economic growth nor do they take into account of the social institutional factor. However, there is one exception. Li and Wu (2010) hypothesize that the negative effects of corruption on economic efficiency can be mitigated by trust. To test this hypothesis, they initially provide a case study and compare the situation in China and Philippines. By introducing the unique “guanxi” culture in China and the high level of public trust arising from it, they explain why China can thrive in spite of the rampant corruption while Philippines cannot. They then conduct a statistical test by utilizing a pooled database of 65 countries across two time periods: 1994-‐1999 and 2000-‐2005 respectively. In particular, they find that the negative impact of corruption will be reduced by 1.495 standard deviations if there is a one-‐standard deviation increase in the public trust level. Both methods support their hypothesis.

Based on Li and Wu (2010)’s study, this paper aims to examine whether the role of public trust has an impact on the corruption-‐economic growth relationship. For this purpose, a structural break model is estimated in order to consider the complementary effects of corruption and public trust. This study is significant in that it uses more recent data in cross-‐country analysis in examining the relationship. In addition, it controls for other institutional factors and restrict this paper in the context of social institution measured by public trust in the structural break model.

3. The Data

3.1 Dependent variable-‐Economic Growth

In this paper, the annual GDP growth rate is adopted to measure economic growth. Financial crisis in the last two decades indicated that the short-‐tem growth rate of GDP in a country would fluctuate depending on country-‐specific conditions; therefore a relatively long-‐term period is more appropriate to remove such fluctuations (Mo, 2011). The period of 1995-‐2010 is chosen for this study. Following the method applied in Mo (2001), the yearly GDP growth rate is estimated by finding compound interest rate r in the equation of GDP95 ∗ 1 + r ^(!/!")_{= GDP10,
where
GDP95
(10)
is
the
real
gross} domestic product in 1995 (2010).

3.2 Independent variables

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The main indicator for measuring corruption is the Corruption Perception index (CPI), which comes from Transparency International. This index has been available since 1995 and it is a composite index based on a combination of various independent and reputable institutions (Transparency International, 2012). It ranks countries according to their perceived level of public-‐sector corruption. The score is from 0 to 10 with 0 indicating that corruption is rampant and dominates the country entirely and 10 indicating a highly clean country17_{.
Note
that
there
are
three
concerns
with
the
measure}

of CPI (Andersson and Heywood, 2009): firstly, CPI only measures the perceived level of corruption but not the real levels; secondly, different institutions use different measures, this may lead to biased results since all surveys measure the CPI based on their own belief of what corruption is; finally, CPI surveys are different from year to year, which make it difficult to obtain a non-‐biased measure when comparing rankings and analyzing trends. Alternative measures such as International Corruption Risk Guide (ICRG) are available. However, problems such as bias still exist. According to de Maria (2008) that CPI is considered to be the most well known and widely measure for corruption. Therefore, CPI is adopted in this paper and calculated by taking the average of CPI score in 1995 and 2010.

3.2.2 Public Trust

One of the most important concerns in this paper is the measurement of public trust. Due to its invisibility and difficulties in defining, there is no common used proxy for it. According to Li and Wu (2010) and La Porta et al (1997), the measure of trust is obtained from World Value Survey18_{based
on
one
specific
question
asked:
“Generally}

speaking, would you say that most people can be trusted or that you cannot be too careful in dealing with people?” There are two options available, either yes or no. Suggested by Li and Filer (2007), the level of public trust is measured by referring the percentage of people answering “yes”. This survey is conducted every 5 years and the number of countries included is limited. In order to remove the problem of small sample size, the data from three periods 1995-‐1999, 2000-‐2004, and 2005-‐2009 is used and countries that appear at least two out of the three periods are selected. The final trust score is calculated by taking the average percentage of people answering “yes”.

17_{WWW.TRANSPARENCY.ORG}

18_{World
Value
survey
(WVS)
is
an
association
originates
in
1982,
conducts
survey
and
aims
to
study
the}

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3.3 Control variables

Due to the fact that corruption and the level of public trust do not determine economic growth solely, therefore the estimation includes several control variables following Mo (2001)’s paper: initial level of real GDP per capita; the rate of population growth; share of investment in GDP and a proxy for human capital.

3.3.1 GDP95: the initial level of real GDP per capita in the year of 1995. According to Barro (1997), the existing level of economic development in a country is important in affecting its economic growth. Thus, real GDP per capita in 1995 is used to estimate the existing development level. The expected sign of GDP95 is negative because there would be a convergence trend due to the knowledge discrepancy between countries in endogenous growth (Barro and Sala-‐I-‐Martin, 1995).

3.3.2 population growth rate: the average growth rate from the year of 1995 to 2010. Studies have shown that population growth also influences economic growth (Barro, 1997). Therefore, in the model it is controlled and the average growth rate from 1995 to 2010 will be used.

3.3.3 investment to GDP ratio: the average ratio from the year of 1995 to 2010.

This ration indicates a country’s investment level and it is robust in affecting economic growth (Mo, 2001).

3.3.4 schooling: the average years of attending school in the total population with an age of over 25 in 1995 and in 2010. According to Barro (1997), economic growth is positively affected by the human capital stock because an educated labor force tends to generate a higher rate of productivity growth.

3.3.5 Political system: dummy variable with 1 indicating Not Free and 0 indicating Free19_{.
Political
system
is
an
indicator
of
political
institution
environment
and
measures}

political rights (Mo, 2011). Corruption is more rampant where other forms of institutional inefficiency exist; therefore, political system is included in this regression in order to capture the freedom statue. In terms of its impact on economic growth, results are mixed. Scholars, for instance Prezeworski et al (2000) find that there is no

19_{FREEDOMHOUSE.ORG}

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significant effect on economic growth while Barro (1997) finds a nonlinear relationship between them.

4. Empirical Analysis

(a) Benchmark regressions

First, the effect of corruption on economic growth is estimated across 41 countries without taking the effect of public trust into account. This estimation approach provides an opportunity to compare the findings with previous cross-‐country empirical analysis. This traditional estimation takes the following form:

1 GDPgr

_!

= α + βControl

_!

+ γCPI

_!

+ ε

_!

Where GDPgr is a dependent variable and indicates the level of economic growth rate in a specific country i, Control is the vector of control variables as described in section 3, and CPI is the measure of corruption levels. The interesting part is the sign of

γ

which might be positive indicating that corruption will adversely affect economic growth, thus supports the conventional viewpoint or negative indicating corruption, which supports the “grease-‐the-‐wheel” view. Table 2 contains the cross-‐sectional results.

The results show that CPI coefficient is insignificant positive in this model specification. For control variables, initial GDP per capital shows a negative relationship with one percent significance level, which corresponds to the previous expectation and earlier studies. Moreover, countries with a higher level of investment to GDP ration show higher economic growth rate with one percent significance level as well. This supports the previous expectation either.

The results might be surprising because a majority of studies find a significant positive effect of corruption on economic growth, which means a highly clean country usually experience higher economic growth. In this regression analysis, the sign of CPI coefficient goes in the same direction, but they are not significant at the conventional

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level. Therefore, the following reasons are proposed to explain the results. First, it is because of the slightly small sample size. For other related studies, they contain a broader database, for example, Pellegrinidhe and Grelagh (2004) considers 64 countries. Second, it may be due to the slightly different timing of those control variables as well as the different measurements used. For example, in this paper, dummy variable is used to indicate different political system while Mo (2001) uses Gastil index20_{to
capture
the}

effect of political system. Another difference is that instead of using a single year for cross-‐country analysis, this paper uses a longer time period to avoid the fluctuations in economic growth rate caused by country-‐specific conditions. All these differences could probably explain the surprising results compared with former studies. Nevertheless, the benchmark results question the simple relationship between corruption and economic growth. In the next sub-‐part, the level of public trust in a county is included in the corruption-‐economic growth relationship analysis in order to reinvestigate whether their relationship depends on trust level.

(b) Cross-‐country analysis considering the role of trust

The hypothesis to be tested in this section is that the relationship between corruption and economic growth depends on the trust level. As discussed in section 2, it is not plausible to examine corruption and economic growth relationship without considering the effect of institution environment. Furthermore, the case of China provides significant importance to examine, specifically the role of trust in their relationship. For this purpose, public trust is included as an indicator of social institutional factor in the relationship between corruption and economic growth (Coleman, 1990). The new estimation takes the following form:

2 GDPgr

!

= α + βControl

!

+ γCPI

!

+ µμTrust

!

+ νTrust ∗ CPI

!

+ ε

!

The interaction term of corruption and trust shows whether the relationship between corruption and economic growth depends on the trust level. In other words, it indicates whether corruption and trust have a complementary impact on economic growth. Before regression, it is important to estimate the correlation coefficients for all

20_{Gastil
index
measure
the
global
freedom
level
with
rating
on
a
scale
of
1(i.e.
the
highest
degree
of}

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the variables because of the multicollinearity issue. Table 3 presents correlation coefficients.

The results show that the correlation between the initial GDP per capita and corruption is 0.82. This might cause multicollinearity problem in the regression estimates. According to Li and Wu (2010) two regressions namely one with and the other without initial GDP per capita should be run separately to test the multicollinearity issue. Therefore, two specifications are warranted to test the hypothesis: Model (2) examines estimation equation (2) and Model (2)* repeats the regression but without the controlling variable: initial GDP per capita 1995. The results are presented in Table 4.

In terms of the key variables of interest, the results in these two models are very consistent: the coefficient of corruption is negative which means it affects economic growth positively; the coefficient of trust and the interaction term is positive which means they affect economic growth in a positive manner. These indicate the relationship between corruption, trust and economic growth are stable and robust (Li and Wu, 2010). Therefore, in order to discuss the results, Model 2 will be used. In this model, the coefficient of Trust is positive but insignificant; which goes against the view of Knack and Keefer (1997) that trust has a strong positive impact on economic performance. This suggests that trust has negligible positive impact on economic growth in this model specification. For corruption, even though the sign changes from positive to negative, the significance level is not high. This means there is no strong impact of corruption on economic growth. For the interaction term, it is positive but insignificant as well, showing that the trust cannot intertwine corruption-‐economic growth relationship. Therefore, the hypothesis that trust could mitigate the adverse impact of corruption on economic growth should be rejected. There are several reasons for it. On one hand, the model specified in this paper is slightly different from previous studies. Interpretations should be made with caution because it might be studied in a partial manner. For example, problems of measurement errors or omitted variable bias can be significant.

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can contribute to other reasons. Shleifer and Vishny (1993) argue that the negative effect of corruption might be mitigated among strong centralized governments, while it is not the case for decentralized governments. Specifically, corruption is part of fixed costs in doing business in centralized environment and the cost associated is predictable and controllable; which makes it less harmful.

5. Concluding Remarks

This study has investigated the role of public trust on corruption and economic growth relationship between the years 1995 to 2010 largely using the method proposed by Li and Wu (2010). It is an augmented version by incorporating more control variables such as investment and significant in that it uses a longer time period. Unlike previous studies, the result in Model (1) reveals an insignificant impact of corruption on economic growth. Several reasons are supposed, for instance, different control variables. Then, a structural break model considering the effect of public trust is estimated. The OLS regression result shows that the coefficient for CPI and interaction term CPI* Trust, which are the key independent variables of interest, proved to be statistically insignificant. This implies that public trust, as a measure of social institutional factors, does not intertwine corruption-‐economic growth relationship. There are several possible explanations listed in Part 4.2, however, the specific reason of this result is beyond the scope of this paper. One limitation of this study is that the measurement of trust and corruption. Even though previous studies have extensively used WVS index and CPI index to measure trust and corruption respectively, it is still far from perfect. For example, the number and content of surveys21_{used
vary
from
one
country
to
another.
This
will
inevitably}

question the quality and reliability of the data and create bias when comparing across countries.

However, I can still put forward some suggestions for further research. First, rather than using Index provided by Transparency International, one could use the number of civil servants who are detected for abusing authority to measure corruption. Fisman and Gatti (2002) point out that it is a more objective and reliable measure. They use it in their decentralization-‐corruption analysis to measure each state’s corruption level in US and obtain satisfying results. Besides, several interesting issues evidenced by this study suggest further investigations. For example, whether the effect of public trust

21_{Both
index
are
survey-‐based.}

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on corruption-‐economic growth relationship depends on condition variable settings in the regression analysis. Testing these models will be an interesting topic since public trust as shown in this paper does not have the same result as previous studies.

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Trust and relationship between corruption and economic growth

Trust and the Relationship between Corruption and Economic

Growth

University of Amsterdam

Bachelor’s thesis: Economics

Min Zhang

Student Number: 10389814

Supervisor: Audrey Hu

June 2015

Abstract

Statement of Originality

The document is written by Min ZHANG who declares to take full responsibility

for the contents of this document.

I declare that the text and the work presented in this document is original and

that no sources other than those mentioned in the text and its references have

been used in creating it.

1. Introduction

2. Literature Review

3. The Data

3.1 Dependent variable-­‐Economic Growth

3.2 Independent variables

3.3 Control variables

4. Empirical Analysis

1 GDPgr

= α + βControl

+ γCPI

+ ε

γ

2 GDPgr

= α + βControl

+ γCPI

+ µμTrust

+ νTrust ∗ CPI

+ ε

5. Concluding Remarks

Bibliography

3.1 Dependent variable-‐Economic Growth